Search Results for author: Yexin Wang

Found 10 papers, 7 papers with code

Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval

no code implementations29 Apr 2022 Shupeng Su, Binjie Zhang, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Ying Shan

The task of privacy-preserving model upgrades in image retrieval desires to reap the benefits of rapidly evolving new models without accessing the raw gallery images.

Image Retrieval

Aesthetic Text Logo Synthesis via Content-aware Layout Inferring

1 code implementation6 Apr 2022 Yizhi Wang, Guo Pu, Wenhan Luo, Yexin Wang, Pengfei Xiong, Hongwen Kang, Zhouhui Lian

To train and evaluate our approach, we construct a dataset named as TextLogo3K, consisting of about 3, 500 text logo images and their pixel-level annotations.

Towards Universal Backward-Compatible Representation Learning

no code implementations3 Mar 2022 Binjie Zhang, Yixiao Ge, Yantao Shen, Shupeng Su, Fanzi Wu, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan

The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features.

Face Recognition Representation Learning

Information Gain Propagation: a new way to Graph Active Learning with Soft Labels

1 code implementation ICLR 2022 Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.

Active Learning

Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval

1 code implementation24 Jan 2022 Binjie Zhang, Yixiao Ge, Yantao Shen, Yu Li, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan

In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly.

Image Retrieval

Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation

1 code implementation16 Dec 2021 Yujia Zhang, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao, Wing-Yin Yu

Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning.

Contrastive Learning Representation Learning +1

3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge

1 code implementation4 Dec 2021 Xinlong Sun, Yangyang Qin, Xuyuan Xu, Guoping Gong, Yang Fang, Yexin Wang

As a basic task of computer vision, image similarity retrieval is facing the challenge of large-scale data and image copy attacks.

Self-Supervised Learning

RIM: Reliable Influence-based Active Learning on Graphs

1 code implementation NeurIPS 2021 Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.

Active Learning

Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval

no code implementations ICLR 2022 Binjie Zhang, Yixiao Ge, Yantao Shen, Yu Li, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan

In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly.

Image Retrieval

Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

1 code implementation31 Jul 2021 Wentao Zhang, Zhi Yang, Yexin Wang, Yu Shen, Yang Li, Liang Wang, Bin Cui

Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.

Active Learning Knowledge Graphs

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